The typical retail organization uses a reorder point system to manage inventories at the store-level. These are execution systems rather than planning systems. They review products and if the on-hand balance is below a preset number (the reorder point), an order is created to replenish inventory. If the on-hand balance is above the reorder point, no further action is taken. No projections are made into the future for the purposes of planning product requirements from the suppliers or manufacturers. No projections are made for the purposes of planning projected inventory levels. These are strictly inventory management systems designed to release orders at the appropriate time.
Manufacturing companies have used time-phased planning systems (also called DRP or Distribution Resource Planning or Distribution Requirements Planning) beginning with implementations in 1975. These systems provide projections into the future which can be used to plan product requirements, transportation requirements, capacity requirements and financial requirements. However, these systems are designed to meet the needs of a manufacturing organization, and have not proved suitable for a retailer's needs. These systems are not able to process the large data volumes typical of most retail organizations. Additionally, manufacturing systems are not designed to deal with fundamental retailing needs such as promotional replenishments, holiday forecasting, shelf configurations, fast easy-to-use displays of information appropriate for large volumes of data, and product groupings where several products are treated as one for the purposes of forecasting and replenishment.
A typical manufacturing organization of the type that would supply retail stores might stock several hundred to a thousand products at one to ten different suppliers. This results in ten thousand stock keeping locations. A typical retailer might stock ten thousand to fifty thousand products in one to several thousand locations. This results in as many as 100 million stock keeping locations. Systems designed for tens of thousands of stock keeping locations could theoretically be made to work for a hundred million stock keeping locations, but as a result of limitations in computing resources and time available to complete the computing operations this has not been achieved in actual practice. There are numerous technical hurdles to overcome in order to process the amount of data for a retail store supply chain during the relatively short time window in which the processing needs to be done. Most retailers get their sales history (or point of sale data or POS) late in the evening or in the early hours of the morning. Several hours later, the replenishment planning must be complete in order to load trucks for deliveries. Additionally, using systems not designed to economically process this amount of data would force the retailer to purchase significantly more computer processing power than would be needed with a system designed for these volumes (assuming such a system existed).
The current and projected retail store shelf arrangements can have a significant effect on the planned replenishment shipments. For example, an increase in shelf space for Christmas products would cause an increase in projected replenishment shipments some number of days before the planned change in shelf arrangement. Similarly, a return to the normal display after the Christmas season would cause a decrease in the projected replenishment shipments some number of days before the planned return to the normal shelf arrangement. Unfortunately, current time-phased planning systems do not provide capabilities for handling changes in shelf arrangements and showing their resultant impact on the replenishment schedules. Consequently, any planned replenishment shipments calculated without accounting for these shelf changes would be inaccurate, and therefore could not be used to plan product needs at suppliers or suppliers, or to show accurate financial projections of inventory levels at the stores and the suppliers.
This is not a problem which manufacturing companies experience. For this reason, current time-phased planning systems do not provide capabilities for dealing with changes in shelf configurations in a retail store.
Retail stores typically sell a small number of products in two or more different packaging configurations, but do all their forecasting and replenishment in terms of one packaging configuration. For example, the sale of cigarettes happens as packs and cartons. Point of sale (POS) information is collected for both packs and cartons. Yet, all forecasting and replenishment planning is done in cartons. It is not efficient for retailers to forecast the sales of packs, and also forecast the sales of cartons. Nor is it efficient to plan the replenishment of packs and also the replenishment of cartons, and then add the two numbers together (accounting for the number of packs per carton).
This is not a problem which manufacturing companies experience. For this reason, current time-phased planning systems do not provide capabilities for handling products sold in several different configurations, but purchased in only one of these configurations.
Retail stores do a significant amount of business around holidays. However, these holidays may not fall in the same week every year. For example, in one year Easter may fall in the 13th week of the year, but in the prior year it fell in the 14th week of the year. The systems in use today use sales history to predict future demand patterns. Consequently, because Easter fell in the 14th week last year, these systems will tend to forecast a sales spike in week 14, not week 13 where Easter will fall this year. Unfortunately, this leaves the store with too little inventory during the week of Easter, and too much inventory the week after. Some systems use multiple years of history to predict future demand patterns. In such a system, if Easter fell in the 14th week last year, and the 15th week the year before, these systems will tend to forecast a flattened sales increase, with half the increase in one week and the other half of the increase in another. This represents an inaccurate forecast since the sales increase will all happen in a single week, and the week will be week 13.
This is not a problem which manufacturing companies experience. They build inventory in anticipation of the holidays, and so it doesn't matter if their forecasting systems spread the peak over one or several weeks. For this reason, current time-phased planning systems do not provide capabilities for correctly representing holiday sales at a retail store.
Many retail stores do a significant amount of business through promotions. In some retailers, promotions account for the majority of sales for many products. However, current time-phased planning systems do not contain the specialized logic needed to plan replenishments for promotions. Each promotion needs a special replenishment order, typically referred to as an initial distribution, to deliver enough product to the store to construct an attractive product display. Additionally, this product must arrive in enough time to allow store employees to set up the display, and also give them time to get an emergency shipment if something were to go wrong and their initial distribution were delayed or otherwise not shipped. For example, a store may insist that the initial distribution shipment arrive four days before the promotion is to begin. This provides time to set up the display, and if the shipment has not arrived by three days before the promotion, there is still time to get another shipment before the promotion begins. Retailers are in a difficult position if a promotion begins and they do not have the quantity of product to support the promotion, and so they need this sort of back-up plan.
This is not a problem which manufacturing companies experience. For this reason, current time-phased planning systems do not provide capabilities for initial distribution shipments.
During the promotion, different replenishment approaches should be used as compared to the non-promoted periods. For example, the concepts of safety stock and safety time have been part of the prior art for some time. Safety stock means having a shipment arrive at the store when the inventory is projected to drop to a specific number of units, 10 for example. Safety time means having a shipment arrive at the store or suppliers a specified number of days before it is needed, 2 days for example. The goal in both cases is to prevent the store or suppliers from going out of stock when there are demands which are greater than the forecast. Safety stock levels are better suited to the normal non-promotional demands at a store because the safety stock is based on the number of products needed to provide an attractive display. Safety time is a better way to deal with demands that vary greatly (such as promotions), since it has the effect of adjusting the safety stock level automatically. The current replenishment systems available to retailers typically allow safety stock and/or safety time. However they do not allow safety stock to be used simultaneously for one type of demand (non-promotional demands for example) and safety time to be used for other types of demands (promotional demands for example on the same product). This forces retailers to use an approach that is not well suited to their business, or to create other systems to compensate for the limitations in the existing systems.
An old rule in forecasting is that early sales results are worth all the statistics in the world. This is the basis of test marketing. Similarly, in the early days of a promotion, the marketplace speaks. Unfortunately, the current forecasting and replenishment systems do not use this early sales history to re-project the promotional forecast, and then re-project the planned replenishments during the promotion period. As a result, this early marketplace information is not used to its full potential to ship the right quantities of product to the correct stores, resulting in overstocks at some stores, and out of stocks at other stores.
DRP and MRP systems are widely used by businesses of varying size in local area network (LAN) environments, and to a lesser extent in browser-based intranet environments. Also, it is known for retail stores to order goods from their suppliers over the Internet using browser-based communication systems. However, browser-based DRP systems designed for use in a LAN, on the Internet or in other networks do not provide the speed and functionality required in a retail store supply chain. Furthermore, a need exists among small retail stores for a browser-based time-phased forecasting and replenishment planning system in which such functions are done for such stores by a third party service provider.